Modeling uncertainty to improve personalized recommendations via Bayesian deep learning

Abstract

Modeling uncertainty has been a major challenge in developing Machine Learning solutions to solve real world problems in various domains. In Recommender Systems, a typical usage of uncertainty is to balance exploration and exploitation, where the uncertainty helps to guide the selection of new options in exploration. Recent advances in combining Bayesian methods with deep learning enable us to express uncertain status in deep learning models. In this paper, we investigate an approach based on Bayesian deep learning to improve personalized recommendations. We first build deep learning architectures to learn useful representation of user and item inputs for predicting their interactions. We then explore multiple embedding components to accommodate different types of user and item inputs. Based on Bayesian deep learning techniques, a key novelty of our approach is to capture the uncertainty associated with the model output and further utilize it to boost exploration in the context of Recommender Systems. We test the proposed approach in both a Collaborative Filtering and a simulated online recommendation setting. Experimental results on publicly available benchmarks demonstrate the benefits of our approach in improving the recommendation performance.